{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:04:36.981591Z",
     "start_time": "2017-12-05T09:04:35.672670Z"
    }
   },
   "outputs": [],
   "source": [
    "from skimage import data, img_as_float\n",
    "from skimage.segmentation import chan_vese, active_contour\n",
    "from skimage import measure\n",
    "from skimage import measure\n",
    "import numpy as np\n",
    "from matplotlib.path import Path\n",
    "import skimage\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import train_cnn\n",
    "import stacked_ae\n",
    "import utils\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T14:51:22.651377Z",
     "start_time": "2017-12-04T14:51:22.648875Z"
    }
   },
   "source": [
    "### Original (from Paper) model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:06:45.109450Z",
     "start_time": "2017-12-05T09:04:53.455486Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Dataset shape :', (495, 64, 64, 1), (495, 1, 32, 32))\n"
     ]
    }
   ],
   "source": [
    "_, X_fullsize, _, contour_mask, y_pred, h, m = train_cnn.run(model='simple', history=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:20:44.286619Z",
     "start_time": "2017-12-05T09:20:21.939108Z"
    }
   },
   "outputs": [],
   "source": [
    "models_sae_loss = stacked_ae.run(X_fullsize, y_pred, contour_mask, history=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:07:30.387804Z",
     "start_time": "2017-12-05T09:07:30.314456Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics_sae = utils.stats_results(models_sae_loss[1], models_sae_loss[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:07:31.316441Z",
     "start_time": "2017-12-05T09:07:31.313243Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DM on Train Set 0.36\n"
     ]
    }
   ],
   "source": [
    "print('DM on Train Set %.2f' % metrics_sae[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:25:40.372138Z",
     "start_time": "2017-12-05T09:25:39.868034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JlByvVCro6+tDV1cXbNuWwk65XMbExAQajQZc14Wu61LUoUm8GIZhGIZhGIZhGGYpwsLN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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14acd1ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(ncols=4, figsize=(20,4))\n",
    "idx = 412\n",
    "X_input = X_fullsize[idx].reshape((256,256))\n",
    "X_roi = stacked_ae.open_data_AE(X_fullsize[idx:idx+1], y_pred[idx:idx+1], contour_mask[idx:idx+1])[0].reshape((64,64))\n",
    "\n",
    "bin_pred = models_sae_loss[2][idx]\n",
    "contours_pred = measure.find_contours(bin_pred, 0.8)\n",
    "contour_pred = contours_pred[np.argmax([k.shape[0] for k in contours_pred])]\n",
    "\n",
    "img = models_sae_loss[0][idx].reshape((64,64))\n",
    "ac_contour = active_contour(img, contour_pred, alpha=0.01, beta=1)\n",
    "\n",
    "ax[0].imshow(X_input, cmap='gray')\n",
    "ax[1].imshow(X_roi, cmap='gray')\n",
    "ax[2].imshow(img, cmap='gray')\n",
    "ax[2].plot(contour_pred[:, 1], contour_pred[:, 0], linewidth=2, color='red',label='Prediction')\n",
    "ax[3].imshow(img, cmap='gray')\n",
    "ax[3].plot(ac_contour[:, 1], ac_contour[:, 0], linewidth=2, color='orange',label='Prediction')\n",
    "for i in range(4):\n",
    "    ax[i].axis('off')\n",
    "plt.savefig('./Rapport/images/final_results.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-05T09:07:14.534806Z",
     "start_time": "2017-12-05T09:06:59.459Z"
    }
   },
   "outputs": [],
   "source": [
    "t = []\n",
    "for idx in range(len(models_sae_loss[2])):\n",
    "    bin_tru = models_sae_loss[1][idx].reshape((64,64))\n",
    "    bin_pred = models_sae_loss[2][idx]\n",
    "    contours_pred = measure.find_contours(bin_pred, 0.8)\n",
    "    contour_pred = contours_pred[np.argmax([k.shape[0] for k in contours_pred])]\n",
    "    img = models_sae_loss[0][idx].reshape((64,64))\n",
    "    ac_contour = active_contour(img, contour_pred, alpha=0.001, beta=0.01) \n",
    "    # create mask contour with experts contours\n",
    "    x, y = np.meshgrid(np.arange(64), np.arange(64)) # make a canvas with coordinates\n",
    "    x, y = x.flatten(), y.flatten()\n",
    "    points = np.vstack((x,y)).T \n",
    "    p = Path(ac_contour) # make a polygon\n",
    "    grid = p.contains_points(points)\n",
    "    mask_contour = grid.reshape(64,64)\n",
    "    mask_contour=mask_contour*1\n",
    "    t.append(utils.dice_metric(mask_contour.T, models_sae_loss[1][idx].reshape((64,64))))\n",
    "print('DM on Train Set after Deformable Models %.2f' % np.mean(t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T21:03:50.998376Z",
     "start_time": "2017-12-04T21:03:28.682784Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Dataset shape :', (524, 64, 64, 1), (524, 1, 32, 32))\n"
     ]
    }
   ],
   "source": [
    "X_test, X_fullsize_test, Y_test, contour_mask_test, y_pred = train_cnn.inference(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T21:03:51.455775Z",
     "start_time": "2017-12-04T21:03:51.000044Z"
    }
   },
   "outputs": [],
   "source": [
    "inference_sae_loss = stacked_ae.inference(X_fullsize_test, y_pred, contour_mask_test, models_sae_loss[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T21:03:51.515724Z",
     "start_time": "2017-12-04T21:03:51.457398Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics_sae_loss_inf = utils.stats_results(inference_sae_loss[1], inference_sae_loss[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T21:04:18.216259Z",
     "start_time": "2017-12-04T21:04:18.213216Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DM on Online Set 0.29\n"
     ]
    }
   ],
   "source": [
    "print('DM on Online Set %.2f' % metrics_sae_loss_inf[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T21:04:29.908157Z",
     "start_time": "2017-12-04T21:04:23.244067Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DM on Train Set after Deformable Models 0.29\n"
     ]
    }
   ],
   "source": [
    "t = []\n",
    "for idx in range(len(inference_sae_loss[2])):\n",
    "    bin_tru = inference_sae_loss[1][idx].reshape((64,64))\n",
    "    bin_pred = inference_sae_loss[2][idx]\n",
    "    contours_pred = measure.find_contours(bin_pred, 0.8)\n",
    "    contour_pred = contours_pred[np.argmax([k.shape[0] for k in contours_pred])]\n",
    "    img = inference_sae_loss[0][idx].reshape((64,64))\n",
    "    ac_contour = active_contour(img, contour_pred, alpha=0.001, beta=0.01) \n",
    "    # create mask contour with experts contours\n",
    "    x, y = np.meshgrid(np.arange(64), np.arange(64)) # make a canvas with coordinates\n",
    "    x, y = x.flatten(), y.flatten()\n",
    "    points = np.vstack((x,y)).T \n",
    "    p = Path(ac_contour) # make a polygon\n",
    "    grid = p.contains_points(points)\n",
    "    mask_contour = grid.reshape(64,64)\n",
    "    mask_contour=mask_contour*1\n",
    "    t.append(utils.dice_metric(mask_contour.T, inference_sae_loss[1][idx].reshape((64,64))))\n",
    "print('DM on Train Set after Deformable Models %.2f' % np.mean(t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T14:33:30.983124Z",
     "start_time": "2017-12-04T14:33:30.802080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Ground Truth')"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQQAAACUCAYAAAB1GVf9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWckFwcxKfwKg598wt9iSMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14974bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 201\n",
    "f, ax = plt.subplots(ncols=2, figsize=(4,8))\n",
    "ax[0].imshow(inference_sae_loss[2][idx])\n",
    "ax[1].imshow(inference_sae_loss[1][idx].reshape((64,64)))\n",
    "ax[1].set_title('Ground Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T14:55:35.245418Z",
     "start_time": "2017-12-04T14:55:34.907596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ozqkqLuBeH92AFp1+ukJ/nWAS1XWr5qXq5/5+qIIsVMnaVWuok9RdN+m6TqCF6jiVk6ET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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x165f578d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_tru = inference_sae_loss[1][idx].reshape((64,64))\n",
    "bin_pred = inference_sae_loss[2][idx]\n",
    "contour_truth = measure.find_contours(bin_tru, 0.8)[0]\n",
    "contours_pred = measure.find_contours(bin_pred, 0.8)\n",
    "contour_pred = contours_pred[np.argmax([k.shape[0] for k in contours_pred])]\n",
    "img = inference_sae_loss[0][idx].reshape((64,64))\n",
    "ac_contour = active_contour(img, contour_pred, alpha=0.005, beta=0.1)\n",
    "\n",
    "f, ax = plt.subplots(ncols=2, figsize=(12,6))\n",
    "ax[0].imshow(img, cmap='gray')\n",
    "ax[0].plot(contour_truth[:, 1], contour_truth[:, 0], linewidth=2, color='green',linestyle='dashed', label='Ground Truth')\n",
    "ax[0].plot(contour_pred[:, 1], contour_pred[:, 0], linewidth=2, color='red',label='Prediction')\n",
    "ax[1].imshow(img, cmap='gray')\n",
    "ax[1].plot(contour_truth[:, 1], contour_truth[:, 0], linewidth=2, color='green',linestyle='dashed', label='Ground Truth')\n",
    "ax[1].plot(ac_contour[:, 1], ac_contour[:, 0], linewidth=2, color='orange', label='Smooth Prediction')\n",
    "ax[1].axis('off')\n",
    "ax[0].axis('off')\n",
    "plt.savefig('./Rapport/images/smoothness.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:06:25.270393Z",
     "start_time": "2017-12-04T17:03:30.526571Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Dataset shape :', (495, 64, 64, 1), (495, 1, 32, 32))\n"
     ]
    }
   ],
   "source": [
    "_, X_fullsize, _, contour_mask, y_pred, h, m = train_cnn.run(model='simple', history=True)\n",
    "models_sae_loss = stacked_ae.run(X_fullsize, y_pred, contour_mask, history=True, loss1='MSE', loss2='MSE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:06:47.267114Z",
     "start_time": "2017-12-04T17:06:25.272399Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Dataset shape :', (524, 64, 64, 1), (524, 1, 32, 32))\n"
     ]
    }
   ],
   "source": [
    "X_test, X_fullsize_test, Y_test, contour_mask_test, y_pred = train_cnn.inference(m)\n",
    "inference_sae_loss_mse = stacked_ae.inference(X_fullsize_test, y_pred, contour_mask_test, models_sae_loss[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:16:44.437463Z",
     "start_time": "2017-12-04T17:16:44.298877Z"
    }
   },
   "outputs": [],
   "source": [
    "metrics_sae_loss_inf = utils.stats_results(inference_sae_loss_mse[1], inference_sae_loss_mse[2])\n",
    "metrics_sae_loss_2 = utils.stats_results(inference_sae_loss[1], inference_sae_loss[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:16:45.025095Z",
     "start_time": "2017-12-04T17:16:45.021911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DM on Online Set 0.41\n"
     ]
    }
   ],
   "source": [
    "print('DM on Online Set %.2f' % metrics_sae_loss_inf[0].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:21:10.668476Z",
     "start_time": "2017-12-04T17:21:10.256819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Truth')"
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zJN+Y5K4kx273vlhjfGcM/8ieneSdSWqMcQ6x3Jjk1C3rRvX6J/nKJB/P0Pdo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\nAAAAIEny/wBMkg0kk8BnPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128ba8ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 268\n",
    "f, ax = plt.subplots(ncols=3, figsize=(20,5))\n",
    "ax[0].imshow(inference_sae_loss_mse[2][idx])\n",
    "ax[0].set_title('MSE')\n",
    "ax[1].imshow(inference_sae_loss[2][idx])\n",
    "ax[1].set_title('Original')\n",
    "ax[2].imshow(inference_sae_loss_mse[1][idx].reshape((64,64)))\n",
    "ax[2].set_title('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:34:46.959827Z",
     "start_time": "2017-12-04T17:34:46.745710Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j6ACfiMX4xcgIf59K8WAoxBafBVbdNFGjHlUzCKCmpqawrarAatQkmCPyVC+Qajro46Ee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\nCSF2CiHuFkJMWaB7fsa3poh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AlwHv8A65C/g08A1TO5Zl+bouVLVuPPW1bcECTt+4kVV9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\ngPg/TgW0YbfR7tQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129dbf9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_tru = inference_sae_loss[1][idx].reshape((64,64))\n",
    "bin_pred_orig = inference_sae_loss[2][idx]\n",
    "bin_pred_mse = inference_sae_loss_mse[2][idx]\n",
    "contour_truth = measure.find_contours(bin_tru, 0.8)[0]\n",
    "contours_pred_orig = measure.find_contours(bin_pred_orig, 0.8)\n",
    "contour_pred_orig = contours_pred_orig[np.argmax([k.shape[0] for k in contours_pred_orig])]\n",
    "contours_pred_mse = measure.find_contours(bin_pred_mse, 0.8)\n",
    "contour_pred_mse = contours_pred_mse[np.argmax([k.shape[0] for k in contours_pred_mse])]\n",
    "img = inference_sae_loss[0][idx].reshape((64,64))\n",
    "ac_contour_mse = active_contour(img, contour_pred_mse, alpha=0.01, beta=10.5)\n",
    "ac_contour_orig = active_contour(img, contour_pred_orig, alpha=0.005, beta=0.1)\n",
    "\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.plot(contour_truth[:, 1], contour_truth[:, 0], linewidth=2, color='green', label='Ground Truth')\n",
    "plt.plot(ac_contour_mse[:, 1], ac_contour_mse[:, 0], linewidth=2, color='orange', label='Prediction All MSE')\n",
    "plt.plot(ac_contour_orig[:, 1], ac_contour_orig[:, 0], linewidth=2, color='red', label='Prediction Original')\n",
    "plt.savefig('./Rapport/images/loss_smooth.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T17:20:37.023723Z",
     "start_time": "2017-12-04T17:20:36.870918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1299e1a50>"
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6V/yecEOz+gHyZ8h6XXga7rOrT6nkQb1O2Huhssbz39qg0dZiRnojVj1mxK1v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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12994bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx=234\n",
    "from skimage import measure\n",
    "# Find contours at a constant value of 0.8\n",
    "contours_truth = measure.find_contours(inference_sae_loss[1][idx].reshape((64,64)), 0.9)\n",
    "contours_simple = measure.find_contours(inference_sae_loss[2][idx], 0.9)\n",
    "contours_mse = measure.find_contours(inference_sae_loss_mse[2][idx], 0.9)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.imshow(inference_sae_loss_mse[0][idx].reshape((64,64)), cmap='gray')\n",
    "\n",
    "for n, contour in enumerate(contours_truth):\n",
    "    ax.plot(contour[:, 1], contour[:, 0], linewidth=2, color='green', label='Ground Truth')\n",
    "\n",
    "for n, contour in enumerate(contours_simple[:1]):\n",
    "    ax.plot(contour[:, 1], contour[:, 0], linewidth=2, color='red', label='Prediction Regular')\n",
    "\n",
    "for n, contour in enumerate(contours_mse[:10]):\n",
    "    ax.plot(contour[:, 1], contour[:, 0], linewidth=2, color='orange', label='Prediction All MSE')\n",
    "    \n",
    "ax.axis('image')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "plt.legend(bbox_to_anchor=(1,1))\n",
    "#plt.savefig('./Rapport/images/image_with_contour_truth_pred_SAE.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Computing APD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T13:46:51.012706Z",
     "start_time": "2017-12-04T13:46:51.009520Z"
    }
   },
   "outputs": [],
   "source": [
    "def closest_node(node, nodes):\n",
    "    nodes = np.asarray(nodes)\n",
    "    deltas = nodes - node\n",
    "    dist_2 = np.einsum('ij,ij->i', deltas, deltas)\n",
    "    return np.argmin(dist_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-04T13:59:48.541349Z",
     "start_time": "2017-12-04T13:59:47.179466Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OZNhUAqEqYyzbuGtrazNsqozRoaHsxZxGo8rGPC4QFxstWHkdNJaE2TUrzspj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\nrjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1497a7d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.plot(contour_truth[:, 1], contour_truth[:, 0], linewidth=2, color='green', label='Ground Truth')\n",
    "plt.plot(ac_contour[:, 1], ac_contour[:, 0], linewidth=2, color='orange', label='Smooth Prediction')\n",
    "for i in range(171):\n",
    "    j = closest_node(ac_contour[i,:], contour_truth)\n",
    "    plt.plot([ac_contour[i,1], contour_truth[j,1]], [ac_contour[i,0], contour_truth[j,0]], marker = '.', color='red')\n",
    "    plt.plot([ac_contour[i,1]], ac_contour[i,0],'y.', linewidth=0.1)\n",
    "    plt.plot([contour_truth[j,1]], contour_truth[j,0],'g.')\n",
    "plt.savefig('./Rapport/images/apd.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
